Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations255347
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.9 MiB
Average record size in memory578.7 B

Variable types

Text1
Numeric7
Categorical7
Boolean3
DateTime1

Alerts

LoanID has unique values Unique

Reproduction

Analysis started2025-09-16 12:14:02.564578
Analysis finished2025-09-16 12:14:26.056489
Duration23.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

LoanID
Text

Unique 

Distinct255347
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.4 MiB
2025-09-16T17:44:26.586726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2553470
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique255347 ?
Unique (%)100.0%

Sample

1st rowI38PQUQS96
2nd rowHPSK72WA7R
3rd rowC1OZ6DPJ8Y
4th rowV2KKSFM3UN
5th rowEY08JDHTZP
ValueCountFrequency (%)
v2kksfm3un 1
 
< 0.1%
zth91cgl0b 1
 
< 0.1%
i38pquqs96 1
 
< 0.1%
fv922axelu 1
 
< 0.1%
xma3ucyp0l 1
 
< 0.1%
bm4b25r7ei 1
 
< 0.1%
y5hyhqwbvp 1
 
< 0.1%
oq99dn6jl4 1
 
< 0.1%
nuui6cjdz9 1
 
< 0.1%
dsl4o0kawd 1
 
< 0.1%
Other values (255337) 255337
> 99.9%
2025-09-16T17:44:27.071425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Q 71348
 
2.8%
U 71325
 
2.8%
C 71163
 
2.8%
E 71147
 
2.8%
M 71117
 
2.8%
I 71117
 
2.8%
L 71097
 
2.8%
6 71071
 
2.8%
O 71046
 
2.8%
9 71032
 
2.8%
Other values (26) 1842007
72.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2553470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 71348
 
2.8%
U 71325
 
2.8%
C 71163
 
2.8%
E 71147
 
2.8%
M 71117
 
2.8%
I 71117
 
2.8%
L 71097
 
2.8%
6 71071
 
2.8%
O 71046
 
2.8%
9 71032
 
2.8%
Other values (26) 1842007
72.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2553470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 71348
 
2.8%
U 71325
 
2.8%
C 71163
 
2.8%
E 71147
 
2.8%
M 71117
 
2.8%
I 71117
 
2.8%
L 71097
 
2.8%
6 71071
 
2.8%
O 71046
 
2.8%
9 71032
 
2.8%
Other values (26) 1842007
72.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2553470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 71348
 
2.8%
U 71325
 
2.8%
C 71163
 
2.8%
E 71147
 
2.8%
M 71117
 
2.8%
I 71117
 
2.8%
L 71097
 
2.8%
6 71071
 
2.8%
O 71046
 
2.8%
9 71032
 
2.8%
Other values (26) 1842007
72.1%

Age
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.498306
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:27.350286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median43
Q356
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.990258
Coefficient of variation (CV)0.34461706
Kurtosis-1.1984306
Mean43.498306
Median Absolute Deviation (MAD)13
Skewness0.00069785437
Sum11107162
Variance224.70785
MonotonicityNot monotonic
2025-09-16T17:44:27.516126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 5064
 
2.0%
40 5056
 
2.0%
65 5027
 
2.0%
33 5022
 
2.0%
53 5010
 
2.0%
62 4999
 
2.0%
34 4987
 
2.0%
45 4985
 
2.0%
61 4982
 
2.0%
39 4973
 
1.9%
Other values (42) 205242
80.4%
ValueCountFrequency (%)
18 4884
1.9%
19 4963
1.9%
20 4861
1.9%
21 4889
1.9%
22 4970
1.9%
23 4740
1.9%
24 4869
1.9%
25 4840
1.9%
26 4891
1.9%
27 4945
1.9%
ValueCountFrequency (%)
69 4817
1.9%
68 4958
1.9%
67 4876
1.9%
66 4841
1.9%
65 5027
2.0%
64 4840
1.9%
63 4862
1.9%
62 4999
2.0%
61 4982
2.0%
60 4772
1.9%

Income
Real number (ℝ)

Distinct114620
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82499.305
Minimum15000
Maximum149999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:27.653656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15000
5-th percentile21780
Q148825.5
median82466
Q3116219
95-th percentile143206
Maximum149999
Range134999
Interquartile range (IQR)67393.5

Descriptive statistics

Standard deviation38963.014
Coefficient of variation (CV)0.47228294
Kurtosis-1.1983609
Mean82499.305
Median Absolute Deviation (MAD)33693
Skewness-0.00038051328
Sum2.106595 × 1010
Variance1.5181164 × 109
MonotonicityNot monotonic
2025-09-16T17:44:27.800157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117102 10
 
< 0.1%
69492 10
 
< 0.1%
121985 10
 
< 0.1%
101884 9
 
< 0.1%
31741 9
 
< 0.1%
126175 9
 
< 0.1%
148090 9
 
< 0.1%
118191 9
 
< 0.1%
77704 9
 
< 0.1%
137221 9
 
< 0.1%
Other values (114610) 255254
> 99.9%
ValueCountFrequency (%)
15000 3
< 0.1%
15001 2
< 0.1%
15002 2
< 0.1%
15003 1
 
< 0.1%
15004 2
< 0.1%
15005 2
< 0.1%
15008 2
< 0.1%
15009 2
< 0.1%
15010 2
< 0.1%
15011 4
< 0.1%
ValueCountFrequency (%)
149999 2
< 0.1%
149997 2
< 0.1%
149996 3
< 0.1%
149995 1
 
< 0.1%
149994 4
< 0.1%
149993 3
< 0.1%
149992 1
 
< 0.1%
149991 2
< 0.1%
149989 2
< 0.1%
149988 1
 
< 0.1%

LoanAmount
Real number (ℝ)

Distinct158729
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127578.87
Minimum5000
Maximum249999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:27.935418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile17168.3
Q166156
median127556
Q3188985
95-th percentile237814.7
Maximum249999
Range244999
Interquartile range (IQR)122829

Descriptive statistics

Standard deviation70840.706
Coefficient of variation (CV)0.55526992
Kurtosis-1.2036799
Mean127578.87
Median Absolute Deviation (MAD)61415
Skewness-0.0018272468
Sum3.2576881 × 1010
Variance5.0184056 × 109
MonotonicityNot monotonic
2025-09-16T17:44:28.066843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221949 8
 
< 0.1%
133724 8
 
< 0.1%
95419 8
 
< 0.1%
183627 7
 
< 0.1%
235258 7
 
< 0.1%
29290 7
 
< 0.1%
173224 7
 
< 0.1%
239944 7
 
< 0.1%
125787 7
 
< 0.1%
111464 7
 
< 0.1%
Other values (158719) 255274
> 99.9%
ValueCountFrequency (%)
5000 1
 
< 0.1%
5001 1
 
< 0.1%
5005 1
 
< 0.1%
5006 1
 
< 0.1%
5009 2
 
< 0.1%
5012 3
< 0.1%
5015 2
 
< 0.1%
5016 1
 
< 0.1%
5017 2
 
< 0.1%
5020 6
< 0.1%
ValueCountFrequency (%)
249999 1
< 0.1%
249998 1
< 0.1%
249997 1
< 0.1%
249996 1
< 0.1%
249993 2
< 0.1%
249992 1
< 0.1%
249990 1
< 0.1%
249989 1
< 0.1%
249988 1
< 0.1%
249986 2
< 0.1%

CreditScore
Real number (ℝ)

Distinct550
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean574.26435
Minimum300
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:28.199608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile327
Q1437
median574
Q3712
95-th percentile822
Maximum849
Range549
Interquartile range (IQR)275

Descriptive statistics

Standard deviation158.90387
Coefficient of variation (CV)0.27670857
Kurtosis-1.2003018
Mean574.26435
Median Absolute Deviation (MAD)137
Skewness0.0046881863
Sum1.4663668 × 108
Variance25250.439
MonotonicityNot monotonic
2025-09-16T17:44:28.324576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
630 528
 
0.2%
445 521
 
0.2%
829 520
 
0.2%
753 519
 
0.2%
670 515
 
0.2%
643 514
 
0.2%
347 514
 
0.2%
775 512
 
0.2%
362 510
 
0.2%
573 508
 
0.2%
Other values (540) 250186
98.0%
ValueCountFrequency (%)
300 484
0.2%
301 460
0.2%
302 451
0.2%
303 489
0.2%
304 456
0.2%
305 473
0.2%
306 478
0.2%
307 481
0.2%
308 411
0.2%
309 462
0.2%
ValueCountFrequency (%)
849 496
0.2%
848 463
0.2%
847 450
0.2%
846 437
0.2%
845 438
0.2%
844 480
0.2%
843 479
0.2%
842 467
0.2%
841 452
0.2%
840 460
0.2%

MonthsEmployed
Real number (ℝ)

Distinct120
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.541976
Minimum0
Maximum119
Zeros2122
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:28.457991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q130
median60
Q390
95-th percentile113
Maximum119
Range119
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.643376
Coefficient of variation (CV)0.58183114
Kurtosis-1.1996325
Mean59.541976
Median Absolute Deviation (MAD)30
Skewness-0.0021416836
Sum15203865
Variance1200.1635
MonotonicityNot monotonic
2025-09-16T17:44:28.592043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 2227
 
0.9%
26 2223
 
0.9%
45 2220
 
0.9%
107 2207
 
0.9%
79 2198
 
0.9%
17 2198
 
0.9%
118 2196
 
0.9%
94 2194
 
0.9%
34 2194
 
0.9%
28 2190
 
0.9%
Other values (110) 233300
91.4%
ValueCountFrequency (%)
0 2122
0.8%
1 2105
0.8%
2 2151
0.8%
3 2167
0.8%
4 2121
0.8%
5 2121
0.8%
6 2137
0.8%
7 2186
0.9%
8 2125
0.8%
9 2116
0.8%
ValueCountFrequency (%)
119 2091
0.8%
118 2196
0.9%
117 2084
0.8%
116 2130
0.8%
115 2084
0.8%
114 2131
0.8%
113 2150
0.8%
112 2175
0.9%
111 2145
0.8%
110 2078
0.8%

NumCreditLines
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
2
64130 
3
63834 
4
63829 
1
63554 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters255347
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
2 64130
25.1%
3 63834
25.0%
4 63829
25.0%
1 63554
24.9%

Length

2025-09-16T17:44:28.709137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:28.803561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 64130
25.1%
3 63834
25.0%
4 63829
25.0%
1 63554
24.9%

Most occurring characters

ValueCountFrequency (%)
2 64130
25.1%
3 63834
25.0%
4 63829
25.0%
1 63554
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 255347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 64130
25.1%
3 63834
25.0%
4 63829
25.0%
1 63554
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 255347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 64130
25.1%
3 63834
25.0%
4 63829
25.0%
1 63554
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 255347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 64130
25.1%
3 63834
25.0%
4 63829
25.0%
1 63554
24.9%

InterestRate
Real number (ℝ)

Distinct2301
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.492773
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:28.946631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.15
Q17.77
median13.46
Q319.25
95-th percentile23.85
Maximum25
Range23
Interquartile range (IQR)11.48

Descriptive statistics

Standard deviation6.6364431
Coefficient of variation (CV)0.49185166
Kurtosis-1.1971672
Mean13.492773
Median Absolute Deviation (MAD)5.74
Skewness0.0046078909
Sum3445339.2
Variance44.042377
MonotonicityNot monotonic
2025-09-16T17:44:29.091615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.92 147
 
0.1%
2.25 144
 
0.1%
4.78 140
 
0.1%
16.44 140
 
0.1%
24 139
 
0.1%
7.3 139
 
0.1%
20.04 138
 
0.1%
8.14 138
 
0.1%
9.14 137
 
0.1%
10.89 137
 
0.1%
Other values (2291) 253948
99.5%
ValueCountFrequency (%)
2 44
 
< 0.1%
2.01 110
< 0.1%
2.02 108
< 0.1%
2.03 104
< 0.1%
2.04 109
< 0.1%
2.05 119
< 0.1%
2.06 102
< 0.1%
2.07 108
< 0.1%
2.08 93
< 0.1%
2.09 121
< 0.1%
ValueCountFrequency (%)
25 53
 
< 0.1%
24.99 96
< 0.1%
24.98 113
< 0.1%
24.97 118
< 0.1%
24.96 126
< 0.1%
24.95 124
< 0.1%
24.94 135
0.1%
24.93 121
< 0.1%
24.92 109
< 0.1%
24.91 117
< 0.1%

LoanTerm
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
48
51166 
60
51154 
36
51061 
24
51009 
12
50957 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters510694
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row60
3rd row24
4th row24
5th row48

Common Values

ValueCountFrequency (%)
48 51166
20.0%
60 51154
20.0%
36 51061
20.0%
24 51009
20.0%
12 50957
20.0%

Length

2025-09-16T17:44:29.234655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:29.313021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
48 51166
20.0%
60 51154
20.0%
36 51061
20.0%
24 51009
20.0%
12 50957
20.0%

Most occurring characters

ValueCountFrequency (%)
6 102215
20.0%
4 102175
20.0%
2 101966
20.0%
8 51166
10.0%
0 51154
10.0%
3 51061
10.0%
1 50957
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 102215
20.0%
4 102175
20.0%
2 101966
20.0%
8 51166
10.0%
0 51154
10.0%
3 51061
10.0%
1 50957
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 102215
20.0%
4 102175
20.0%
2 101966
20.0%
8 51166
10.0%
0 51154
10.0%
3 51061
10.0%
1 50957
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 102215
20.0%
4 102175
20.0%
2 101966
20.0%
8 51166
10.0%
0 51154
10.0%
3 51061
10.0%
1 50957
10.0%

DTIRatio
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50021206
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-09-16T17:44:29.440826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.14
Q10.3
median0.5
Q30.7
95-th percentile0.86
Maximum0.9
Range0.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.23091662
Coefficient of variation (CV)0.46163744
Kurtosis-1.1996748
Mean0.50021206
Median Absolute Deviation (MAD)0.2
Skewness-0.0014989634
Sum127727.65
Variance0.053322483
MonotonicityNot monotonic
2025-09-16T17:44:29.597332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67 3385
 
1.3%
0.64 3308
 
1.3%
0.37 3288
 
1.3%
0.19 3285
 
1.3%
0.13 3285
 
1.3%
0.4 3280
 
1.3%
0.86 3274
 
1.3%
0.78 3271
 
1.3%
0.73 3269
 
1.3%
0.76 3265
 
1.3%
Other values (71) 222437
87.1%
ValueCountFrequency (%)
0.1 1611
0.6%
0.11 3051
1.2%
0.12 3224
1.3%
0.13 3285
1.3%
0.14 3228
1.3%
0.15 3162
1.2%
0.16 3131
1.2%
0.17 3230
1.3%
0.18 3117
1.2%
0.19 3285
1.3%
ValueCountFrequency (%)
0.9 1605
0.6%
0.89 3134
1.2%
0.88 3168
1.2%
0.87 3152
1.2%
0.86 3274
1.3%
0.85 3139
1.2%
0.84 3207
1.3%
0.83 3233
1.3%
0.82 3222
1.3%
0.81 3221
1.3%

Education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.9 MiB
Bachelor's
64366 
High School
63903 
Master's
63541 
PhD
63537 

Length

Max length11
Median length10
Mean length8.0107932
Min length3

Characters and Unicode

Total characters2045532
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelor's
2nd rowMaster's
3rd rowMaster's
4th rowHigh School
5th rowBachelor's

Common Values

ValueCountFrequency (%)
Bachelor's 64366
25.2%
High School 63903
25.0%
Master's 63541
24.9%
PhD 63537
24.9%

Length

2025-09-16T17:44:29.869249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:29.980541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bachelor's 64366
20.2%
high 63903
20.0%
school 63903
20.0%
master's 63541
19.9%
phd 63537
19.9%

Most occurring characters

ValueCountFrequency (%)
h 255709
12.5%
o 192172
 
9.4%
s 191448
 
9.4%
c 128269
 
6.3%
l 128269
 
6.3%
e 127907
 
6.3%
a 127907
 
6.3%
' 127907
 
6.3%
r 127907
 
6.3%
B 64366
 
3.1%
Other values (9) 573671
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2045532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 255709
12.5%
o 192172
 
9.4%
s 191448
 
9.4%
c 128269
 
6.3%
l 128269
 
6.3%
e 127907
 
6.3%
a 127907
 
6.3%
' 127907
 
6.3%
r 127907
 
6.3%
B 64366
 
3.1%
Other values (9) 573671
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2045532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 255709
12.5%
o 192172
 
9.4%
s 191448
 
9.4%
c 128269
 
6.3%
l 128269
 
6.3%
e 127907
 
6.3%
a 127907
 
6.3%
' 127907
 
6.3%
r 127907
 
6.3%
B 64366
 
3.1%
Other values (9) 573671
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2045532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 255709
12.5%
o 192172
 
9.4%
s 191448
 
9.4%
c 128269
 
6.3%
l 128269
 
6.3%
e 127907
 
6.3%
a 127907
 
6.3%
' 127907
 
6.3%
r 127907
 
6.3%
B 64366
 
3.1%
Other values (9) 573671
28.0%

EmploymentType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.4 MiB
Part-time
64161 
Unemployed
63824 
Self-employed
63706 
Full-time
63656 

Length

Max length13
Median length9
Mean length10.247902
Min length9

Characters and Unicode

Total characters2616771
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFull-time
2nd rowFull-time
3rd rowUnemployed
4th rowFull-time
5th rowUnemployed

Common Values

ValueCountFrequency (%)
Part-time 64161
25.1%
Unemployed 63824
25.0%
Self-employed 63706
24.9%
Full-time 63656
24.9%

Length

2025-09-16T17:44:30.101514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:30.210273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
part-time 64161
25.1%
unemployed 63824
25.0%
self-employed 63706
24.9%
full-time 63656
24.9%

Most occurring characters

ValueCountFrequency (%)
e 446583
17.1%
l 318548
12.2%
m 255347
9.8%
t 191978
 
7.3%
- 191523
 
7.3%
i 127817
 
4.9%
o 127530
 
4.9%
y 127530
 
4.9%
d 127530
 
4.9%
p 127530
 
4.9%
Other values (9) 574855
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2616771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 446583
17.1%
l 318548
12.2%
m 255347
9.8%
t 191978
 
7.3%
- 191523
 
7.3%
i 127817
 
4.9%
o 127530
 
4.9%
y 127530
 
4.9%
d 127530
 
4.9%
p 127530
 
4.9%
Other values (9) 574855
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2616771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 446583
17.1%
l 318548
12.2%
m 255347
9.8%
t 191978
 
7.3%
- 191523
 
7.3%
i 127817
 
4.9%
o 127530
 
4.9%
y 127530
 
4.9%
d 127530
 
4.9%
p 127530
 
4.9%
Other values (9) 574855
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2616771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 446583
17.1%
l 318548
12.2%
m 255347
9.8%
t 191978
 
7.3%
- 191523
 
7.3%
i 127817
 
4.9%
o 127530
 
4.9%
y 127530
 
4.9%
d 127530
 
4.9%
p 127530
 
4.9%
Other values (9) 574855
22.0%

MaritalStatus
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
Married
85302 
Divorced
85033 
Single
85012 

Length

Max length8
Median length7
Mean length7.0000822
Min length6

Characters and Unicode

Total characters1787450
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowMarried
3rd rowDivorced
4th rowMarried
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married 85302
33.4%
Divorced 85033
33.3%
Single 85012
33.3%

Length

2025-09-16T17:44:30.332486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:30.416994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 85302
33.4%
divorced 85033
33.3%
single 85012
33.3%

Most occurring characters

ValueCountFrequency (%)
r 255637
14.3%
i 255347
14.3%
e 255347
14.3%
d 170335
9.5%
a 85302
 
4.8%
M 85302
 
4.8%
D 85033
 
4.8%
v 85033
 
4.8%
o 85033
 
4.8%
c 85033
 
4.8%
Other values (4) 340048
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1787450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 255637
14.3%
i 255347
14.3%
e 255347
14.3%
d 170335
9.5%
a 85302
 
4.8%
M 85302
 
4.8%
D 85033
 
4.8%
v 85033
 
4.8%
o 85033
 
4.8%
c 85033
 
4.8%
Other values (4) 340048
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1787450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 255637
14.3%
i 255347
14.3%
e 255347
14.3%
d 170335
9.5%
a 85302
 
4.8%
M 85302
 
4.8%
D 85033
 
4.8%
v 85033
 
4.8%
o 85033
 
4.8%
c 85033
 
4.8%
Other values (4) 340048
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1787450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 255637
14.3%
i 255347
14.3%
e 255347
14.3%
d 170335
9.5%
a 85302
 
4.8%
M 85302
 
4.8%
D 85033
 
4.8%
v 85033
 
4.8%
o 85033
 
4.8%
c 85033
 
4.8%
Other values (4) 340048
19.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size249.5 KiB
True
127677 
False
127670 
ValueCountFrequency (%)
True 127677
50.0%
False 127670
50.0%
2025-09-16T17:44:30.485585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size249.5 KiB
True
127742 
False
127605 
ValueCountFrequency (%)
True 127742
50.0%
False 127605
50.0%
2025-09-16T17:44:30.546141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

LoanPurpose
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
Business
51298 
Home
51286 
Education
51005 
Other
50914 
Auto
50844 

Length

Max length9
Median length8
Mean length6.0017114
Min length4

Characters and Unicode

Total characters1532519
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowAuto
4th rowBusiness
5th rowAuto

Common Values

ValueCountFrequency (%)
Business 51298
20.1%
Home 51286
20.1%
Education 51005
20.0%
Other 50914
19.9%
Auto 50844
19.9%

Length

2025-09-16T17:44:30.688375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:30.772597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
business 51298
20.1%
home 51286
20.1%
education 51005
20.0%
other 50914
19.9%
auto 50844
19.9%

Most occurring characters

ValueCountFrequency (%)
s 153894
 
10.0%
e 153498
 
10.0%
u 153147
 
10.0%
o 153135
 
10.0%
t 152763
 
10.0%
n 102303
 
6.7%
i 102303
 
6.7%
B 51298
 
3.3%
m 51286
 
3.3%
H 51286
 
3.3%
Other values (8) 407606
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1532519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 153894
 
10.0%
e 153498
 
10.0%
u 153147
 
10.0%
o 153135
 
10.0%
t 152763
 
10.0%
n 102303
 
6.7%
i 102303
 
6.7%
B 51298
 
3.3%
m 51286
 
3.3%
H 51286
 
3.3%
Other values (8) 407606
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1532519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 153894
 
10.0%
e 153498
 
10.0%
u 153147
 
10.0%
o 153135
 
10.0%
t 152763
 
10.0%
n 102303
 
6.7%
i 102303
 
6.7%
B 51298
 
3.3%
m 51286
 
3.3%
H 51286
 
3.3%
Other values (8) 407606
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1532519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 153894
 
10.0%
e 153498
 
10.0%
u 153147
 
10.0%
o 153135
 
10.0%
t 152763
 
10.0%
n 102303
 
6.7%
i 102303
 
6.7%
B 51298
 
3.3%
m 51286
 
3.3%
H 51286
 
3.3%
Other values (8) 407606
26.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size249.5 KiB
True
127701 
False
127646 
ValueCountFrequency (%)
True 127701
50.0%
False 127646
50.0%
2025-09-16T17:44:30.853287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
0
225694 
1
29653 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters255347
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 225694
88.4%
1 29653
 
11.6%

Length

2025-09-16T17:44:31.053607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:44:31.132277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 225694
88.4%
1 29653
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 225694
88.4%
1 29653
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 255347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 225694
88.4%
1 29653
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 255347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 225694
88.4%
1 29653
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 255347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 225694
88.4%
1 29653
 
11.6%
Distinct2191
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2013-01-01 00:00:00
Maximum2018-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-16T17:44:31.308980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:31.818719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-09-16T17:44:23.305134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:16.522115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:17.520143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.625967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:19.823803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.830737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:21.907076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:23.497574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:16.682981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:17.661333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.766501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:19.958898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.982876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:22.112248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:23.701671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:16.825646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:17.876561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.912276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.102526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:21.139254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:22.304395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:23.920454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:16.962751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.028113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:19.055097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.243393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:21.299129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:22.500551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:24.137565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:17.097893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.175334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:19.209390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.377433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:21.450418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:22.711539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:24.292076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:17.237716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.323761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:19.393587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.529121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:21.590850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:22.913099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:24.443065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:17.370834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:18.469041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:19.676839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:20.685579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:21.761248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:44:23.098672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-16T17:44:32.180182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCreditScoreDTIRatioDefaultEducationEmploymentTypeHasCoSignerHasDependentsHasMortgageIncomeInterestRateLoanAmountLoanPurposeLoanTermMaritalStatusMonthsEmployedNumCreditLines
Age1.000-0.001-0.0050.1700.0000.0000.0000.0020.000-0.001-0.001-0.0020.0000.0020.000-0.0000.000
CreditScore-0.0011.000-0.0010.0340.0000.0040.0000.0060.000-0.0010.0000.0010.0020.0000.0030.0010.000
DTIRatio-0.005-0.0011.0000.0200.0020.0000.0000.0000.0060.0000.0010.0010.0020.0040.0000.0020.000
Default0.1700.0340.0201.0000.0290.0450.0390.0350.0230.1240.1320.0860.0220.0000.0280.0970.028
Education0.0000.0000.0020.0291.0000.0000.0020.0000.0000.0040.0040.0040.0010.0010.0040.0010.000
EmploymentType0.0000.0040.0000.0450.0001.0000.0000.0000.0000.0000.0000.0000.0000.0010.0020.0030.000
HasCoSigner0.0000.0000.0000.0390.0020.0001.0000.0000.0030.0000.0040.0000.0000.0000.0010.0000.004
HasDependents0.0020.0060.0000.0350.0000.0000.0001.0000.0000.0050.0000.0000.0030.0010.0000.0030.000
HasMortgage0.0000.0000.0060.0230.0000.0000.0030.0001.0000.0030.0000.0000.0010.0030.0000.0000.000
Income-0.001-0.0010.0000.1240.0040.0000.0000.0050.0031.000-0.002-0.0010.0000.0030.0000.0030.000
InterestRate-0.0010.0000.0010.1320.0040.0000.0040.0000.000-0.0021.000-0.0020.0000.0000.0040.0000.000
LoanAmount-0.0020.0010.0010.0860.0040.0000.0000.0000.000-0.001-0.0021.0000.0000.0000.0030.0030.000
LoanPurpose0.0000.0020.0020.0220.0010.0000.0000.0030.0010.0000.0000.0001.0000.0000.0000.0030.000
LoanTerm0.0020.0000.0040.0000.0010.0010.0000.0010.0030.0030.0000.0000.0001.0000.0030.0000.000
MaritalStatus0.0000.0030.0000.0280.0040.0020.0010.0000.0000.0000.0040.0030.0000.0031.0000.0000.000
MonthsEmployed-0.0000.0010.0020.0970.0010.0030.0000.0030.0000.0030.0000.0030.0030.0000.0001.0000.003
NumCreditLines0.0000.0000.0000.0280.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0031.000

Missing values

2025-09-16T17:44:24.733271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-16T17:44:25.207243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LoanIDAgeIncomeLoanAmountCreditScoreMonthsEmployedNumCreditLinesInterestRateLoanTermDTIRatioEducationEmploymentTypeMaritalStatusHasMortgageHasDependentsLoanPurposeHasCoSignerDefaultLoan Date (DD/MM/YYYY)
0I38PQUQS9656859945058752080415.23360.44Bachelor'sFull-timeDivorcedYesYesOtherYes010/15/2018
1HPSK72WA7R69504321244404581514.81600.68Master'sFull-timeMarriedNoNoOtherYes03/25/2016
2C1OZ6DPJ8Y468420812918845126321.17240.31Master'sUnemployedDivorcedYesYesAutoNo111/11/2013
3V2KKSFM3UN323171344799743037.07240.23High SchoolFull-timeMarriedNoNoBusinessNo06/22/2017
4EY08JDHTZP60204379139633846.51480.73Bachelor'sUnemployedDivorcedNoYesAutoNo06/9/2014
5A9S62RQ7US25902989044872018222.72240.10High SchoolUnemployedSingleYesNoBusinessYes12/16/2018
6H8GXPAOS713811118817702542980119.11120.16Bachelor'sUnemployedSingleYesNoHomeYes08/13/2017
70HGZQKJ36W561268021555115316748.15600.43PhDFull-timeMarriedNoNoHomeYes04/11/2014
81R0N3LGNRJ36420539235782783123.94480.20Bachelor'sSelf-employedDivorcedYesNoEducationNo111/23/2015
9CM9L1GTT2P4013278422851048011449.09480.33High SchoolSelf-employedMarriedYesNoOtherYes012/12/2018
LoanIDAgeIncomeLoanAmountCreditScoreMonthsEmployedNumCreditLinesInterestRateLoanTermDTIRatioEducationEmploymentTypeMaritalStatusHasMortgageHasDependentsLoanPurposeHasCoSignerDefaultLoan Date (DD/MM/YYYY)
255337DSL4O0KAWD6473743140354300024.12120.24PhDSelf-employedSingleYesNoEducationYes010/27/2016
2553386V8S5IUS6368217111682313527829.71600.36PhDFull-timeDivorcedYesYesHomeNo010/22/2015
255339O6SWO6CBGB516949212296234866210.83480.27High SchoolPart-timeDivorcedNoNoHomeNo011/9/2016
25534048LOOK4VR1416180911923844434219.99360.31Master'sPart-timeMarriedYesYesAutoYes03/13/2016
255341AKXAXQN7PG401298901161197013839.91240.23High SchoolPart-timeDivorcedYesNoHomeYes15/30/2017
2553428C6S86ESGC1937979210682541109414.11120.85Bachelor'sFull-timeMarriedNoNoOtherNo010/7/2017
25534398R4KDHNND325195318989951114211.55240.21High SchoolPart-timeDivorcedNoNoHomeNo11/1/2016
255344XQK1UUUNGP56848202082945977035.29600.50High SchoolSelf-employedMarriedYesYesAutoYes03/5/2017
255345JAO28CPL4H42851096057580940120.90480.44High SchoolPart-timeSingleYesYesOtherNo04/5/2013
255346ZTH91CGL0B62224181848163611326.73120.48Bachelor'sUnemployedDivorcedYesNoEducationYes01/27/2015